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https://github.com/LovelyBuggies/Awesome_ML

Some good resources to study machine learning 📖
https://github.com/LovelyBuggies/Awesome_ML

List: Awesome_ML

awesome-list machine-learning

Last synced: 16 days ago
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Some good resources to study machine learning 📖

Host: GitHub
URL: https://github.com/LovelyBuggies/Awesome_ML
Owner: LovelyBuggies
License: mit
Created: 2021-04-29T04:24:53.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2022-01-21T03:32:15.000Z (almost 3 years ago)
Last Synced: 2024-05-23T00:06:58.549Z (7 months ago)
Topics: awesome-list, machine-learning
Homepage:
Size: 6.92 MB
Stars: 7
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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ultimate-awesome - Awesome_ML - Some good resources to study machine learning 📖. (Other Lists / Monkey C Lists)

README

        # Awesome_ML

- Stuffs about Machine Learning.

- List some **interesting** resources to study Machine Learning here.

- Mostly are posts and surveys, but also include interesting discussion and interdisciplinary works.

- Welcome to contribute(**PR**) and please give a reason why it's interesting.

**Table of Contents**

- [Awesome_ML](#awesome-ml)

  * [Mathematics Basics](#mathematics-basics)

  * [Physics Basics](#physics-basics)

  * [Tensor Theory](#tensor-theory)

  * [Machine Learning Basics](#machine-learning-basics)

  * [Computer Vision](#computer-vision)

  * [Natural Language Processing](#natural-language-processing)

  * [Distributed Deep Learning](#distributed-deep-learning)

  * [Control Theory and Reinforcement Learning](#control-theory-and-reinforcement-learning)

  * [Adversial Learning and Imitation Learning](#adversial-learning-and-imitation-learning)

  * [Opportunities](#opportunities)

  * [Miscellaneous](#miscellaneous)

Generated with markdown-toc

## Mathematics Basics

- [fast.ai: numerical-linear-algebra](https://github.com/fastai/numerical-linear-algebra)

- [Einstein Summation in Deep Learning](https://rockt.github.io/2018/04/30/einsum)

## Physics Basics

- [QC for ML: A Survey](https://arxiv.org/pdf/2006.12025.pdf), _a great survey_

- [UIUC: QC and Shor's Algorithm](https://quantum-algorithms.herokuapp.com/299/paper/paper.html)

- [IIT: Seminar on Quantum Computing](https://cse.iitkgp.ac.in/~goutam/quantumComputing/index.html), _a brief intro to quantum computing_

- [UT: Quantum Machine Learning(YouTube)](https://www.youtube.com/playlist?list=PLmRxgFnCIhaMgvot-Xuym_hn69lmzIokg)

## Tensor Theory

- [MPI: Introduction to Tensors](https://www.mpi-inf.mpg.de/fileadmin/inf/d5/teaching/ws15_16_adamant/tensor_intro.pdf)

- [Tensor Network](https://tensornetwork.org/)

- [Tensors.net](https://www.tensors.net/)

- [Tensor Decompositions and Applications](https://www.cs.cmu.edu/~christos/courses/826-resources/PAPERS+BOOK/Kolda-Bader-SAND2007-6702.pdf)

- [TN VS ML](http://txiang.iphy.ac.cn/mlreview.pdf), _connection between TN and ML_

## Machine Learning Basics

To be an expert in a specified field of ML, you must lay a solid foundation of the basics:

- [UCB: ML@Berkley](https://ml.berkeley.edu/blog/)

- [林轩田机器学习基础(bilibili)](https://www.bilibili.com/video/BV1Cx411i7op?from=search&seid=15028071265755754643)

- [Columbia: CS4771 Machine Learning](http://www.cs.columbia.edu/~verma/classes/ml/index.html)

- [Cheat sheet for Data Scientists](./cheat_sheet_for_Data_Scientists.pdf)

## Computer Vision

## Natural Language Processing

## Distributed Deep Learning

- [Ring AllReduce](https://towardsdatascience.com/visual-intuition-on-ring-allreduce-for-distributed-deep-learning-d1f34b4911da), _a straightforward tutorial for beginners: share reduce and share only_

## Control Theory and Reinforcement Learning

- [UIUC: Calculus of Variations and Optimal Control Theory](http://liberzon.csl.illinois.edu/teaching/cvoc/cvoc.html)

- [Cool Things in Optimal Control and Physics](https://cgliu.github.io/posts/optimal-control/optimization-physics.html#org1237ec1)

- [HJB Equations & Stochastic Differential Equations](https://benjaminmoll.com/wp-content/uploads/2019/07/Lecture4_ECO521_web.pdf), _details about Hamiltonian v.s. Bellman_

- [OpenAI: Spinning Up](https://spinningup.openai.com/en/latest/index.html)

- [Stanford: Reinforcement Learning An Introduction](https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf)

- [UCB: CS294 Deep Reinforcement Learning](http://rail.eecs.berkeley.edu/deeprlcourse-fa17/)

- [强化学习线路](https://mp.weixin.qq.com/s/E2va_w2Lh_x3n_1XnOY0ZA), _containing some flaws_

- [Policy Gradient Algorithms](https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html#what-is-policy-gradient)

- [DRL & Meta-Learning Series](https://jonathan-hui.medium.com/rl-deep-reinforcement-learning-series-833319a95530)

- [Understanding AC Methods](https://towardsdatascience.com/understanding-actor-critic-methods-931b97b6df3f)

- [Distributional RL](https://mtomassoli.github.io/2017/12/08/distributional_rl/)

- [TD error vs advantage vs Bellman error](http://boris-belousov.net/2017/04/29/ergodic-MDP/)

## Adversial Learning and Imitation Learning

- [GAN VS Actor-Critic](https://arxiv.org/abs/1610.01945)

- [KD 20篇 paper 回顾](https://zhuanlan.zhihu.com/p/160206075)

- [KD: A Survey](https://arxiv.org/abs/2006.05525)

- [KD and S-T Learning for CV: A Review](https://arxiv.org/abs/2004.05937)

## Opportunities

ML is such a promising field that contains a lot of opportunities for students:

- [BAIR](https://bair.berkeley.edu/getting_involved.html)

- [OpenAI Jobs](https://openai.com/jobs/)

- [DeepMind Research Jobs](https://deepmind.com/careers/jobs?teams=Research)

- [Top AI Fellowship](https://towardsdatascience.com/top-ai-fellowship-programs-to-look-out-for-344af565824c)

- [Fellowship & Award Opportunities](http://ml.gatech.edu/content/fellowship-award-opportunities)

- [Open Source Projects](https://github.com/tapaswenipathak/Open-Source-Programs)

- [Apple Career for ML](https://www.apple.com/careers/us/machine-learning-and-ai.html)

- [AIRS Research](https://airs.cuhk.edu.cn/zh-hans/airs/rpositions)

- [IDEA Research](https://idea.edu.cn/)

- [MSRA Jobs](https://www.msra.cn/zh-cn/jobs)

- [Google Research](https://research.google/)

- [Facebook AI](https://ai.facebook.com/)

- [ByteDance AI Lab](https://ailab.bytedance.com/research)

- [Tencent AI Lab](https://ai.tencent.com/ailab/en/index)

- [Amazon Science](https://www.amazon.science/machine-learning)

- [Insight Research Centre](https://www.insight-centre.org/work-with-us/)

## Miscellaneous

- [AI Conference Due](https://aideadlin.es/?sub=ML,CV,NLP,RO,SP,DM)

- [Tips for Research](https://ruder.io/10-tips-for-research-and-a-phd/), _a fantastic blog_